Loading…

Fitting spending functions

Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2010-02, Vol.29 (3), p.321-327
Main Authors: Anderson, Keaven M., Clark, Jason B.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283
cites cdi_FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283
container_end_page 327
container_issue 3
container_start_page 321
container_title Statistics in medicine
container_volume 29
creator Anderson, Keaven M.
Clark, Jason B.
description Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using α‐ and β‐spending functions (Biometrika 1983; 70:659–663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses. This paper explores fitting α‐ and β‐spending functions that have specific values at specific interim analyses. Commonly used one‐parameter families may not provide an adequate fit to more than one desired critical value. We define new one‐ and two‐parameter families to provide additional flexibility along with examples to demonstrate their usefulness. The logistic family is one of these two‐parameter families, which has been applied in several trials. Copyright © 2009 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/sim.3737
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_733700143</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1943330801</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283</originalsourceid><addsrcrecordid>eNp10M1LwzAYBvAgiptT8OxBxIteOt83aZr2KNN9wFSYisfQpqlk9mNrWnT_vRkrCoKnBPLjycNDyCnCEAHojTXFkAkm9kgfIRIeUB7ukz5QIbxAIO-RI2uXAIicikPSwyj0KUTYJ2dj0zSmfL-wK12m20vWlqoxVWmPyUEW51afdOeAvI7vX0ZTb_40mY1u555iIRce80UUhwkVmqKvhB8LVEmCLPMDlSANeRbEKagUtGuDKYgkyVAx930UME5DNiBXu9xVXa1bbRtZGKt0nselrlorBWPCVfeZk5d_5LJq69KVk5Qy9JFzcOh6h1RdWVvrTK5qU8T1RiLI7VrSrSW3azl63uW1SaHTX9jN44C3A58m15t_g-Tz7KEL7Lyxjf768XH9IQP3zuXb40TSxQLolN1JYN8cKX7o</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>223141550</pqid></control><display><type>article</type><title>Fitting spending functions</title><source>Wiley-Blackwell Read &amp; Publish Collection</source><creator>Anderson, Keaven M. ; Clark, Jason B.</creator><creatorcontrib>Anderson, Keaven M. ; Clark, Jason B.</creatorcontrib><description>Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using α‐ and β‐spending functions (Biometrika 1983; 70:659–663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses. This paper explores fitting α‐ and β‐spending functions that have specific values at specific interim analyses. Commonly used one‐parameter families may not provide an adequate fit to more than one desired critical value. We define new one‐ and two‐parameter families to provide additional flexibility along with examples to demonstrate their usefulness. The logistic family is one of these two‐parameter families, which has been applied in several trials. Copyright © 2009 John Wiley &amp; Sons, Ltd.</description><identifier>ISSN: 0277-6715</identifier><identifier>EISSN: 1097-0258</identifier><identifier>DOI: 10.1002/sim.3737</identifier><identifier>PMID: 19842091</identifier><identifier>CODEN: SMEDDA</identifier><language>eng</language><publisher>Chichester, UK: John Wiley &amp; Sons, Ltd</publisher><subject>Clinical trials ; Clinical Trials as Topic - standards ; Decision Making ; design ; Flexibility ; group sequential methods ; Humans ; Logistic Models ; spending function ; Statistical analysis</subject><ispartof>Statistics in medicine, 2010-02, Vol.29 (3), p.321-327</ispartof><rights>Copyright © 2009 John Wiley &amp; Sons, Ltd.</rights><rights>(c) 2009 John Wiley &amp; Sons, Ltd.</rights><rights>Copyright John Wiley and Sons, Limited Feb 10, 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283</citedby><cites>FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19842091$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Anderson, Keaven M.</creatorcontrib><creatorcontrib>Clark, Jason B.</creatorcontrib><title>Fitting spending functions</title><title>Statistics in medicine</title><addtitle>Statist. Med</addtitle><description>Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using α‐ and β‐spending functions (Biometrika 1983; 70:659–663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses. This paper explores fitting α‐ and β‐spending functions that have specific values at specific interim analyses. Commonly used one‐parameter families may not provide an adequate fit to more than one desired critical value. We define new one‐ and two‐parameter families to provide additional flexibility along with examples to demonstrate their usefulness. The logistic family is one of these two‐parameter families, which has been applied in several trials. Copyright © 2009 John Wiley &amp; Sons, Ltd.</description><subject>Clinical trials</subject><subject>Clinical Trials as Topic - standards</subject><subject>Decision Making</subject><subject>design</subject><subject>Flexibility</subject><subject>group sequential methods</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>spending function</subject><subject>Statistical analysis</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp10M1LwzAYBvAgiptT8OxBxIteOt83aZr2KNN9wFSYisfQpqlk9mNrWnT_vRkrCoKnBPLjycNDyCnCEAHojTXFkAkm9kgfIRIeUB7ukz5QIbxAIO-RI2uXAIicikPSwyj0KUTYJ2dj0zSmfL-wK12m20vWlqoxVWmPyUEW51afdOeAvI7vX0ZTb_40mY1u555iIRce80UUhwkVmqKvhB8LVEmCLPMDlSANeRbEKagUtGuDKYgkyVAx930UME5DNiBXu9xVXa1bbRtZGKt0nselrlorBWPCVfeZk5d_5LJq69KVk5Qy9JFzcOh6h1RdWVvrTK5qU8T1RiLI7VrSrSW3azl63uW1SaHTX9jN44C3A58m15t_g-Tz7KEL7Lyxjf768XH9IQP3zuXb40TSxQLolN1JYN8cKX7o</recordid><startdate>20100210</startdate><enddate>20100210</enddate><creator>Anderson, Keaven M.</creator><creator>Clark, Jason B.</creator><general>John Wiley &amp; Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>20100210</creationdate><title>Fitting spending functions</title><author>Anderson, Keaven M. ; Clark, Jason B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Clinical trials</topic><topic>Clinical Trials as Topic - standards</topic><topic>Decision Making</topic><topic>design</topic><topic>Flexibility</topic><topic>group sequential methods</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>spending function</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anderson, Keaven M.</creatorcontrib><creatorcontrib>Clark, Jason B.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anderson, Keaven M.</au><au>Clark, Jason B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fitting spending functions</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Statist. Med</addtitle><date>2010-02-10</date><risdate>2010</risdate><volume>29</volume><issue>3</issue><spage>321</spage><epage>327</epage><pages>321-327</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><coden>SMEDDA</coden><abstract>Group sequential monitoring is used to provide guidance on stopping a clinical trial in progress based on interim evaluation of its efficacy objectives. A trial could stop because an experimental regimen (1) is efficacious, (2) lacks any sign of efficacy, or (3) is specifically less efficacious than a control. Group sequential methods using α‐ and β‐spending functions (Biometrika 1983; 70:659–663) are often used to create stopping boundaries for test statistics for efficacy hypotheses computed at interim analyses. This paper explores fitting α‐ and β‐spending functions that have specific values at specific interim analyses. Commonly used one‐parameter families may not provide an adequate fit to more than one desired critical value. We define new one‐ and two‐parameter families to provide additional flexibility along with examples to demonstrate their usefulness. The logistic family is one of these two‐parameter families, which has been applied in several trials. Copyright © 2009 John Wiley &amp; Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley &amp; Sons, Ltd</pub><pmid>19842091</pmid><doi>10.1002/sim.3737</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0277-6715
ispartof Statistics in medicine, 2010-02, Vol.29 (3), p.321-327
issn 0277-6715
1097-0258
language eng
recordid cdi_proquest_miscellaneous_733700143
source Wiley-Blackwell Read & Publish Collection
subjects Clinical trials
Clinical Trials as Topic - standards
Decision Making
design
Flexibility
group sequential methods
Humans
Logistic Models
spending function
Statistical analysis
title Fitting spending functions
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T02%3A14%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fitting%20spending%20functions&rft.jtitle=Statistics%20in%20medicine&rft.au=Anderson,%20Keaven%20M.&rft.date=2010-02-10&rft.volume=29&rft.issue=3&rft.spage=321&rft.epage=327&rft.pages=321-327&rft.issn=0277-6715&rft.eissn=1097-0258&rft.coden=SMEDDA&rft_id=info:doi/10.1002/sim.3737&rft_dat=%3Cproquest_cross%3E1943330801%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3857-3479a8b27e214c74a71cbb13f46cb1285f6ad0cd0e2771d07bbf1c32099635283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=223141550&rft_id=info:pmid/19842091&rfr_iscdi=true